- 首先这是一个成语填字游戏，大概就是一张成语填字游戏图片，通过opencv图像识别后转为矩阵，再通过解算法，解出答案，在显示到图片上。
image
本文采用投影分割法对印刷体汉字进行分割。
投影分割是先水平方向投影，在竖直方向投影，或者先竖直方向再水平方向投影。本文选用先竖直，再水平。
- 竖直投影。
代码：
<pre name="code" class="python" style="box-sizing: border-box; margin: 0px 0px 24px; padding: 0px 16px; overflow-x: auto; background-color: rgb(240, 240, 240); font-family: Consolas, Inconsolata, Courier, monospace; font-size: 12px; line-height: 20px; color: rgb(0, 0, 0); font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: 2; text-align: justify; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px;">#针对的是印刷版的汉字，所以采用了投影法分割
#此函数是行分割，结果是一行文字
def YShadow(path):
img = cv2.imread(path) #原图像
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #灰度图像
height,width = img.shape[:2]
#blur = cv2.GaussianBlur(gray,(5,5),0) #高斯模糊
blur = cv2.blur(gray,(8,8)) #均值模糊
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2) #自适应阈值分割
temp = thresh
if(width > 500 and height > 400): #图像字体较小时，需要进行膨胀操作
kernel = np.ones((5,5),np.uint8) #卷积核
dilation = cv2.dilate(thresh,kernel,iterations = 1) #膨胀操作使得单个文字图像被黑像素填充
temp = dilation
'''
cv2.imshow('image',temp)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
perPixelValue = 1 #每个像素的值
projectValArry = np.zeros(width, np.int8) #创建一个用于储存每列黑色像素个数的数组
for i in range(0,height):
for j in range(0,width):
perPixelValue = temp[i,j]
if (perPixelValue == 255): #如果是黑字，对应位置的值+1
projectValArry[i] += 1
# print(projectValArry[i])
canvas = np.zeros((height,width), dtype="uint8")
for i in range(0,height):
for j in range(0,width):
perPixelValue = 255 #白色背景
canvas[i, j] = perPixelValue
for i in range(0,height):
for j in range(0,projectValArry[i]):
perPixelValue = 0 #黑色直方图投影
canvas[i, width-j-1] = perPixelValue
'''
cv2.imshow('canvas',canvas)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
list = []
startIndex = 0 #记录进入字符区的索引
endIndex = 0 #记录进入空白区域的索引
inBlock = 0 #是否遍历到了字符区内
for i in range(height):
if (inBlock == 0 and projectValArry[i] != 0): #进入字符区
inBlock = 1
startIndex = i
elif (inBlock == 1 and projectValArry[i] == 0):#进入空白区
endIndex = i
inBlock = 0
subImg = gray[startIndex:endIndex+1,0:width] #将对应字的图片截取下来
#print(startIndex,endIndex+1)
list.append(subImg)#添加这个字图像到list
#print(len(list))
return list</pre>
- 水平投影
#对行字进行单个字的分割
def XShadow(path):
img = cv2.imread(path)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
height,width = img.shape[:2]
# print(height,width)
#blur = cv2.GaussianBlur(gray,(5,5),0)
blur = cv2.blur(gray,(8,8))
thresh = cv2.adaptiveThreshold(blur,255,1,1,11,2)
if(width > 500):
kernel = np.ones((4, 4),np.uint8) #卷积核
else:
kernel = np.ones((2, 2),np.uint8) #卷积核
dilation = cv2.dilate(thresh,kernel,iterations = 1) #膨胀操作使得单个文字图像被黑像素填充
'''
cv2.imshow('image',thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
perPixelValue = 1 #每个像素的值
projectValArry = np.zeros(width, np.int8) #创建一个用于储存每列黑色像素个数的数组
for i in range(0,width):
for j in range(0,height):
perPixelValue = dilation[j,i]
if (perPixelValue == 255): #如果是黑字
projectValArry[i] += 1
# print(projectValArry[i])
canvas = np.zeros((height,width), dtype="uint8")
for i in range(0,width):
for j in range(0,height):
perPixelValue = 255 #白色背景
canvas[j, i] = perPixelValue
for i in range(0,width):
for j in range(0,projectValArry[i]):
perPixelValue = 0 #黑色直方图投影
canvas[height-j-1, i] = perPixelValue
'''
cv2.imshow('canvas',canvas)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
list = []
startIndex = 0 #记录进入字符区的索引
endIndex = 0 #记录进入空白区域的索引
inBlock = 0 #是否遍历到了字符区内
for i in range(width):
if (inBlock == 0 and projectValArry[i] != 0): #进入字符区
inBlock = 1
startIndex = i
elif (inBlock == 1 and projectValArry[i] == 0): #进入投影区
endIndex = i
inBlock = 0
#subImg = gray[0:height, startIndex:endIndex+1] #endIndex+1
#print(startIndex,endIndex+1)
list.append([startIndex, 0, endIndex-startIndex-1, height])
#print(len(list))
return list</pre>
分割完后，将对应图片样本存储到对应文件夹，每个字共10种样本
将这些样本及标记保存后，分别加载到samples.npy, label.npy中。供后续的机器学习算法训练使用。
下篇讲解填字图片汉字的提取与机器学习算法训练样本，识别汉字的过程。
